# 大模型增强模块
import os
import time
import logging
import json
from typing import Dict, Any, Optional, List
import threading
import concurrent.futures
from abc import ABC, abstractmethod

# 配置日志
logging.basicConfig(
    level=logging.INFO,
    format='%(asctime)s - %(name)s - %(levelname)s - %(message)s'
)
logger = logging.getLogger("model_enhancer")

# 模型类型枚举
class ModelType:
    LLM = "llm"
    LIGHTWEIGHT = "lightweight"
    SPECIALIZED = "specialized"

# 基础模型接口
class ModelInterface(ABC):
    """模型接口基类"""
    
    @abstractmethod
    def initialize(self, config: Dict[str, Any]) -> bool:
        """初始化模型"""
        pass
    
    @abstractmethod
    def generate(self, prompt: str, params: Dict[str, Any]) -> Dict[str, Any]:
        """生成响应"""
        pass
    
    @abstractmethod
    def evaluate(self, test_cases: List[Dict[str, Any]]) -> Dict[str, float]:
        """评估模型性能"""
        pass
    
    @abstractmethod
    def get_info(self) -> Dict[str, Any]:
        """获取模型信息"""
        pass

# 轻量级模型实现（用于简单命令识别）
class LightweightModel(ModelInterface):
    """轻量级模型实现"""
    
    def __init__(self):
        self.name = "lightweight_model"
        self.type = ModelType.LIGHTWEIGHT
        self.is_initialized = False
        self.config = {}
        self.load_time = 0
    
    def initialize(self, config: Dict[str, Any]) -> bool:
        try:
            start_time = time.time()
            self.config = config
            # 模拟模型加载
            time.sleep(0.1)
            self.load_time = time.time() - start_time
            self.is_initialized = True
            logger.info(f"轻量级模型初始化成功，耗时: {self.load_time:.2f}秒")
            return True
        except Exception as e:
            logger.error(f"轻量级模型初始化失败: {str(e)}")
            return False
    
    def generate(self, prompt: str, params: Dict[str, Any]) -> Dict[str, Any]:
        if not self.is_initialized:
            return {"success": False, "error": "模型未初始化"}
        
        try:
            start_time = time.time()
            
            # 简单命令识别逻辑（示例）
            commands = {
                "打开": "open_application",
                "关闭": "close_application",
                "搜索": "search_files",
                "创建": "create_folder",
                "删除": "delete_file",
                "复制": "copy_file",
                "移动": "move_file",
                "播放": "play_workflow",
                "录制": "start_workflow_recording",
                "提取": "extract_text"
            }
            
            command = None
            for keyword, cmd in commands.items():
                if keyword in prompt:
                    command = cmd
                    break
            
            if command:
                response = {
                    "command": command,
                    "confidence": 0.85,
                    "parameters": {}
                }
            else:
                response = {
                    "command": "unknown",
                    "confidence": 0.1,
                    "parameters": {}
                }
            
            latency = time.time() - start_time
            return {
                "success": True,
                "response": response,
                "latency": latency,
                "model": self.name
            }
        except Exception as e:
            logger.error(f"轻量级模型生成失败: {str(e)}")
            return {"success": False, "error": str(e)}
    
    def evaluate(self, test_cases: List[Dict[str, Any]]) -> Dict[str, float]:
        correct = 0
        total = len(test_cases)
        
        for test_case in test_cases:
            result = self.generate(test_case["input"], {})
            if result["success"] and result["response"]["command"] == test_case["expected"]:
                correct += 1
        
        accuracy = correct / total if total > 0 else 0
        return {
            "accuracy": accuracy,
            "latency": self.load_time,
            "throughput": 100.0  # 模拟每秒处理100个请求
        }
    
    def get_info(self) -> Dict[str, Any]:
        return {
            "name": self.name,
            "type": self.type,
            "initialized": self.is_initialized,
            "config": self.config,
            "load_time": self.load_time
        }

# 本地大模型实现 - LLaMA（模拟）
class LLaMAModel(ModelInterface):
    """LLaMA模型实现"""
    
    def __init__(self):
        self.name = "llama_model"
        self.type = ModelType.LLM
        self.is_initialized = False
        self.config = {}
        self.load_time = 0
        self.model_path = ""
    
    def initialize(self, config: Dict[str, Any]) -> bool:
        try:
            start_time = time.time()
            self.config = config
            self.model_path = config.get("model_path", "")
            
            # 模拟模型加载（实际上应该调用真实的LLaMA模型加载）
            # 在实际实现中，这里应该使用llama-cpp-python或类似库
            time.sleep(0.5)  # 模拟加载时间
            
            self.load_time = time.time() - start_time
            self.is_initialized = True
            logger.info(f"LLaMA模型初始化成功，路径: {self.model_path}，耗时: {self.load_time:.2f}秒")
            return True
        except Exception as e:
            logger.error(f"LLaMA模型初始化失败: {str(e)}")
            return False
    
    def generate(self, prompt: str, params: Dict[str, Any]) -> Dict[str, Any]:
        if not self.is_initialized:
            return {"success": False, "error": "模型未初始化"}
        
        try:
            start_time = time.time()
            
            # 模拟LLaMA模型生成（实际实现中应该调用真实模型）
            # 这里仅作示例，实际使用时应替换为实际的模型调用代码
            time.sleep(0.2)  # 模拟推理时间
            
            # 生成模拟响应
            response_text = f"[LLaMA] 处理复杂指令: {prompt[:50]}..."
            
            latency = time.time() - start_time
            return {
                "success": True,
                "response": {
                    "text": response_text,
                    "tokens_generated": len(response_text) // 4,
                    "command": "complex_task"
                },
                "latency": latency,
                "model": self.name
            }
        except Exception as e:
            logger.error(f"LLaMA模型生成失败: {str(e)}")
            return {"success": False, "error": str(e)}
    
    def evaluate(self, test_cases: List[Dict[str, Any]]) -> Dict[str, float]:
        # 模拟评估结果
        return {
            "accuracy": 0.88,
            "latency": self.load_time + 0.2,
            "throughput": 10.0  # 模拟每秒处理10个请求
        }
    
    def get_info(self) -> Dict[str, Any]:
        return {
            "name": self.name,
            "type": self.type,
            "initialized": self.is_initialized,
            "model_path": self.model_path,
            "config": self.config,
            "load_time": self.load_time
        }

# 本地大模型实现 - Mistral（模拟）
class MistralModel(ModelInterface):
    """Mistral模型实现"""
    
    def __init__(self):
        self.name = "mistral_model"
        self.type = ModelType.LLM
        self.is_initialized = False
        self.config = {}
        self.load_time = 0
        self.model_path = ""
    
    def initialize(self, config: Dict[str, Any]) -> bool:
        try:
            start_time = time.time()
            self.config = config
            self.model_path = config.get("model_path", "")
            
            # 模拟模型加载（实际上应该调用真实的Mistral模型加载）
            time.sleep(0.4)  # 模拟加载时间
            
            self.load_time = time.time() - start_time
            self.is_initialized = True
            logger.info(f"Mistral模型初始化成功，路径: {self.model_path}，耗时: {self.load_time:.2f}秒")
            return True
        except Exception as e:
            logger.error(f"Mistral模型初始化失败: {str(e)}")
            return False
    
    def generate(self, prompt: str, params: Dict[str, Any]) -> Dict[str, Any]:
        if not self.is_initialized:
            return {"success": False, "error": "模型未初始化"}
        
        try:
            start_time = time.time()
            
            # 模拟Mistral模型生成
            time.sleep(0.15)  # 模拟推理时间
            
            # 生成模拟响应
            response_text = f"[Mistral] 智能分析: {prompt[:50]}..."
            
            latency = time.time() - start_time
            return {
                "success": True,
                "response": {
                    "text": response_text,
                    "tokens_generated": len(response_text) // 4,
                    "command": "complex_task"
                },
                "latency": latency,
                "model": self.name
            }
        except Exception as e:
            logger.error(f"Mistral模型生成失败: {str(e)}")
            return {"success": False, "error": str(e)}
    
    def evaluate(self, test_cases: List[Dict[str, Any]]) -> Dict[str, float]:
        # 模拟评估结果
        return {
            "accuracy": 0.91,
            "latency": self.load_time + 0.15,
            "throughput": 12.0  # 模拟每秒处理12个请求
        }
    
    def get_info(self) -> Dict[str, Any]:
        return {
            "name": self.name,
            "type": self.type,
            "initialized": self.is_initialized,
            "model_path": self.model_path,
            "config": self.config,
            "load_time": self.load_time
        }

# 模型缓存管理器
class ModelCacheManager:
    """模型缓存管理器"""
    
    def __init__(self, max_cache_size: int = 100, ttl: int = 300):
        self.max_cache_size = max_cache_size
        self.ttl = ttl  # 缓存过期时间（秒）
        self.cache = {}
        self.cache_lock = threading.RLock()
        self.cleanup_interval = 60  # 清理间隔（秒）
        self._start_cleanup_thread()
    
    def _start_cleanup_thread(self):
        """启动缓存清理线程"""
        def cleanup_task():
            while True:
                time.sleep(self.cleanup_interval)
                self._cleanup_expired()
        
        cleanup_thread = threading.Thread(target=cleanup_task, daemon=True)
        cleanup_thread.start()
    
    def _cleanup_expired(self):
        """清理过期缓存"""
        current_time = time.time()
        with self.cache_lock:
            expired_keys = [k for k, v in self.cache.items() 
                           if current_time - v["timestamp"] > self.ttl]
            for key in expired_keys:
                del self.cache[key]
            logger.info(f"缓存清理完成，移除了 {len(expired_keys)} 个过期项")
    
    def get(self, key: str) -> Optional[Any]:
        """获取缓存"""
        with self.cache_lock:
            if key in self.cache:
                item = self.cache[key]
                if time.time() - item["timestamp"] <= self.ttl:
                    return item["value"]
                else:
                    del self.cache[key]  # 删除过期项
        return None
    
    def set(self, key: str, value: Any) -> None:
        """设置缓存"""
        with self.cache_lock:
            # 如果缓存已满，移除最早的项
            if len(self.cache) >= self.max_cache_size:
                oldest_key = min(self.cache.keys(), 
                                key=lambda k: self.cache[k]["timestamp"])
                del self.cache[oldest_key]
            
            self.cache[key] = {
                "value": value,
                "timestamp": time.time()
            }
    
    def clear(self) -> None:
        """清空缓存"""
        with self.cache_lock:
            self.cache.clear()
    
    def get_stats(self) -> Dict[str, Any]:
        """获取缓存统计信息"""
        with self.cache_lock:
            current_time = time.time()
            valid_count = sum(1 for v in self.cache.values() 
                             if current_time - v["timestamp"] <= self.ttl)
            return {
                "total_size": len(self.cache),
                "valid_entries": valid_count,
                "max_size": self.max_cache_size,
                "ttl": self.ttl
            }

# 模型评估器
class ModelEvaluator:
    """模型评估器"""
    
    def __init__(self, test_cases_dir: str = "model_test_cases"):
        self.test_cases_dir = test_cases_dir
        self.evaluation_results = {}
        self.test_cases = []
        self._load_test_cases()
    
    def _load_test_cases(self):
        """加载测试用例"""
        try:
            if not os.path.exists(self.test_cases_dir):
                os.makedirs(self.test_cases_dir)
                # 创建默认测试用例
                default_cases = [
                    {"input": "打开记事本", "expected": "open_application"},
                    {"input": "搜索文档", "expected": "search_files"},
                    {"input": "创建新文件夹", "expected": "create_folder"},
                    {"input": "删除临时文件", "expected": "delete_file"},
                    {"input": "复制文件到桌面", "expected": "copy_file"},
                    {"input": "移动文件夹到D盘", "expected": "move_file"},
                    {"input": "播放工作流", "expected": "play_workflow"},
                    {"input": "开始录制操作", "expected": "start_workflow_recording"},
                    {"input": "提取屏幕文本", "expected": "extract_text"}
                ]
                
                with open(os.path.join(self.test_cases_dir, "default_cases.json"), "w", encoding="utf-8") as f:
                    json.dump(default_cases, f, ensure_ascii=False, indent=2)
                
                self.test_cases = default_cases
            else:
                # 加载所有测试用例文件
                for filename in os.listdir(self.test_cases_dir):
                    if filename.endswith(".json"):
                        try:
                            with open(os.path.join(self.test_cases_dir, filename), "r", encoding="utf-8") as f:
                                cases = json.load(f)
                                self.test_cases.extend(cases)
                        except Exception as e:
                            logger.error(f"加载测试用例文件 {filename} 失败: {str(e)}")
        except Exception as e:
            logger.error(f"加载测试用例失败: {str(e)}")
    
    def evaluate_model(self, model: ModelInterface) -> Dict[str, float]:
        """评估单个模型"""
        try:
            results = model.evaluate(self.test_cases)
            self.evaluation_results[model.name] = {
                **results,
                "timestamp": time.time()
            }
            logger.info(f"模型 {model.name} 评估完成: {results}")
            return results
        except Exception as e:
            logger.error(f"评估模型 {model.name} 失败: {str(e)}")
            return {"error": str(e)}
    
    def get_best_model(self, metric: str = "accuracy") -> str:
        """获取最佳模型"""
        if not self.evaluation_results:
            return "lightweight_model"  # 默认使用轻量级模型
        
        # 根据指定指标排序
        sorted_models = sorted(
            self.evaluation_results.items(),
            key=lambda x: x[1].get(metric, 0),
            reverse=(metric != "latency")  # 延迟越低越好，其他指标越高越好
        )
        
        best_model = sorted_models[0][0]
        logger.info(f"基于 {metric} 指标，最佳模型是: {best_model}")
        return best_model
    
    def run_evaluation(self, models: List[ModelInterface]) -> Dict[str, Dict[str, float]]:
        """并行评估多个模型"""
        results = {}
        
        with concurrent.futures.ThreadPoolExecutor() as executor:
            future_to_model = {executor.submit(self.evaluate_model, model): model for model in models}
            for future in concurrent.futures.as_completed(future_to_model):
                model = future_to_model[future]
                try:
                    model_results = future.result()
                    results[model.name] = model_results
                except Exception as e:
                    logger.error(f"模型 {model.name} 评估异常: {str(e)}")
                    results[model.name] = {"error": str(e)}
        
        return results

# 模型管理器
class ModelManager:
    """模型管理器"""
    
    def __init__(self, config_path: str = "model_config.json"):
        self.config_path = config_path
        self.models = {}
        self.active_model = "lightweight_model"
        self.cache_manager = ModelCacheManager()
        self.evaluator = ModelEvaluator()
        self.model_lock = threading.RLock()
        self.config = self._load_config()
        self._initialize_models()
    
    def _load_config(self) -> Dict[str, Any]:
        """加载模型配置"""
        default_config = {
            "models": {
                "lightweight_model": {
                    "type": ModelType.LIGHTWEIGHT,
                    "enabled": True,
                    "config": {}
                },
                "llama_model": {
                    "type": ModelType.LLM,
                    "enabled": False,
                    "model_path": "",
                    "config": {
                        "temperature": 0.7,
                        "max_tokens": 1024
                    }
                },
                "mistral_model": {
                    "type": ModelType.LLM,
                    "enabled": False,
                    "model_path": "",
                    "config": {
                        "temperature": 0.7,
                        "max_tokens": 1024
                    }
                }
            },
            "model_selection": {
                "auto_select": True,
                "primary_metric": "accuracy",
                "min_complexity_score": 0.6
            },
            "caching": {
                "enabled": True,
                "max_size": 100,
                "ttl": 300
            }
        }
        
        try:
            if os.path.exists(self.config_path):
                with open(self.config_path, "r", encoding="utf-8") as f:
                    user_config = json.load(f)
                    # 合并用户配置和默认配置
                    return self._merge_configs(default_config, user_config)
        except Exception as e:
            logger.error(f"加载模型配置失败: {str(e)}")
        
        return default_config
    
    def _merge_configs(self, default: Dict[str, Any], user: Dict[str, Any]) -> Dict[str, Any]:
        """合并配置"""
        result = default.copy()
        for key, value in user.items():
            if key in result and isinstance(result[key], dict) and isinstance(value, dict):
                result[key] = self._merge_configs(result[key], value)
            else:
                result[key] = value
        return result
    
    def _save_config(self):
        """保存配置"""
        try:
            with open(self.config_path, "w", encoding="utf-8") as f:
                json.dump(self.config, f, ensure_ascii=False, indent=2)
        except Exception as e:
            logger.error(f"保存模型配置失败: {str(e)}")
    
    def _initialize_models(self):
        """初始化模型"""
        # 初始化轻量级模型（默认启用）
        lightweight_config = self.config["models"].get("lightweight_model", {})
        if lightweight_config.get("enabled", True):
            model = LightweightModel()
            model.initialize(lightweight_config.get("config", {}))
            self.models[model.name] = model
        
        # 初始化LLaMA模型
        llama_config = self.config["models"].get("llama_model", {})
        if llama_config.get("enabled", False):
            model = LLaMAModel()
            config = llama_config.get("config", {})
            config["model_path"] = llama_config.get("model_path", "")
            model.initialize(config)
            self.models[model.name] = model
        
        # 初始化Mistral模型
        mistral_config = self.config["models"].get("mistral_model", {})
        if mistral_config.get("enabled", False):
            model = MistralModel()
            config = mistral_config.get("config", {})
            config["model_path"] = mistral_config.get("model_path", "")
            model.initialize(config)
            self.models[model.name] = model
        
        logger.info(f"模型初始化完成，已加载 {len(self.models)} 个模型")
    
    def _calculate_complexity_score(self, prompt: str) -> float:
        """计算提示词复杂度分数"""
        # 简单的复杂度计算逻辑
        length = len(prompt)
        sentence_count = prompt.count("。") + prompt.count("!") + prompt.count("?")
        complexity = min(1.0, (length / 200) + (sentence_count / 5))
        return complexity
    
    def _select_model(self, prompt: str) -> str:
        """根据提示词选择合适的模型"""
        if not self.config["model_selection"]["auto_select"]:
            return self.active_model
        
        complexity = self._calculate_complexity_score(prompt)
        min_complexity = self.config["model_selection"]["min_complexity_score"]
        
        if complexity < min_complexity:
            # 简单命令使用轻量级模型
            if "lightweight_model" in self.models:
                return "lightweight_model"
        
        # 复杂任务选择性能最好的大模型
        best_model = self.evaluator.get_best_model(self.config["model_selection"]["primary_metric"])
        return best_model if best_model in self.models else "lightweight_model"
    
    def generate(self, prompt: str, params: Dict[str, Any] = None) -> Dict[str, Any]:
        """生成响应"""
        if params is None:
            params = {}
        
        # 检查缓存
        use_cache = params.pop("use_cache", self.config["caching"]["enabled"])
        cache_key = f"{prompt}:{json.dumps(params, sort_keys=True)}"
        
        if use_cache:
            cached_response = self.cache_manager.get(cache_key)
            if cached_response:
                logger.info(f"缓存命中: {cache_key[:50]}...")
                return cached_response
        
        # 选择模型
        model_name = params.pop("model", None) or self._select_model(prompt)
        
        with self.model_lock:
            if model_name not in self.models:
                logger.error(f"模型 {model_name} 未加载")
                return {"success": False, "error": f"模型 {model_name} 未加载"}
            
            model = self.models[model_name]
            # 生成响应
            response = model.generate(prompt, params)
        
        # 缓存响应
        if use_cache and response.get("success", False):
            self.cache_manager.set(cache_key, response)
        
        return response
    
    def set_active_model(self, model_name: str) -> bool:
        """设置当前活动模型"""
        if model_name in self.models:
            self.active_model = model_name
            logger.info(f"已设置活动模型: {model_name}")
            return True
        return False
    
    def get_model_info(self, model_name: str = None) -> Dict[str, Any]:
        """获取模型信息"""
        if model_name:
            if model_name in self.models:
                return self.models[model_name].get_info()
            return {"error": "模型不存在"}
        
        # 返回所有模型信息
        return {
            "active_model": self.active_model,
            "models": {name: model.get_info() for name, model in self.models.items()},
            "cache_stats": self.cache_manager.get_stats()
        }
    
    def reload_model(self, model_name: str, config: Dict[str, Any] = None) -> bool:
        """重新加载模型"""
        with self.model_lock:
            try:
                # 移除旧模型
                if model_name in self.models:
                    del self.models[model_name]
                
                # 创建并初始化新模型
                if model_name == "lightweight_model":
                    model = LightweightModel()
                elif model_name == "llama_model":
                    model = LLaMAModel()
                elif model_name == "mistral_model":
                    model = MistralModel()
                else:
                    return False
                
                # 使用提供的配置或默认配置
                if config:
                    model.initialize(config)
                elif model_name in self.config["models"]:
                    model_config = self.config["models"][model_name]
                    init_config = model_config.get("config", {})
                    if model_name != "lightweight_model":
                        init_config["model_path"] = model_config.get("model_path", "")
                    model.initialize(init_config)
                
                self.models[model_name] = model
                logger.info(f"模型 {model_name} 重新加载成功")
                return True
            except Exception as e:
                logger.error(f"重新加载模型 {model_name} 失败: {str(e)}")
                return False
    
    def run_model_evaluation(self) -> Dict[str, Dict[str, float]]:
        """运行模型评估"""
        models = list(self.models.values())
        if not models:
            return {"error": "没有可用模型"}
        
        return self.evaluator.run_evaluation(models)

# 初始化模型管理器
def initialize_model_manager(config_path: str = None) -> ModelManager:
    """初始化模型管理器，处理None路径情况"""
    # 处理None路径情况，使用默认配置文件
    if config_path is None:
        config_path = os.path.join(os.path.dirname(os.path.abspath(__file__)), "model_config.json")
    return ModelManager(config_path)

# 示例使用
if __name__ == "__main__":
    # 初始化模型管理器
    model_manager = initialize_model_manager()
    
    # 生成响应示例
    simple_prompt = "打开记事本"
    complex_prompt = "请帮我分析一下当前系统状态，然后创建一个新的工作流来自动化日常任务，包括打开应用、处理文件和发送邮件。"
    
    print("\n=== 简单命令测试 ===")
    result1 = model_manager.generate(simple_prompt)
    print(f"提示: {simple_prompt}")
    print(f"响应: {result1}")
    
    print("\n=== 复杂命令测试 ===")
    result2 = model_manager.generate(complex_prompt)
    print(f"提示: {complex_prompt[:50]}...")
    print(f"响应: {result2}")
    
    print("\n=== 模型信息 ===")
    info = model_manager.get_model_info()
    print(f"模型信息: {info}")